-Parametric
techniques based on an interpretation of technical factors not linked to the
signal itself, but rather to the way it has been processed and transported
inside the network.

Inside these two
families, methods have been recently developed, which are particularly
accurate, allowing pertinent prediction and estimation of perceived voice
quality.

More recently, new
approaches have been developed, called “hybrid” because they combine
measurement on signal and parametric indications, in particular in the context
of voice over IP. The complementarities of signal-based and parametric families
of methods make it possible (in theory) to envisage a combination of their
respective advantages: accuracy for signal based techniques, and capacity of
parametric tools to be implemented without constraint on CPU or on signal
decoding. Furthermore, parametric methods bring elements of understanding about
the technical underlying causes (e.g. packet losses may explain cuts in the
signal).

But all these
methods have a common drawback: they do not allow a link between the perceived
impairments and their origins. Some academic studies can be quoted on this, but
without real result until now. From an operational point of view, this is however
the real goal of any assessment technique to find the causes for issues and
propose fixes.

The basic idea behind this new study is that it
is now realistic to envisage providing operational supervision teams with
powerful diagnostic tools able to give them an expert view of the perceived
voice quality impairments on telephone communications and to troubleshoot these
impairments deep in detail.

The objective of this study is therefore the
development of such a tool, combining analysis of the audio signal and
interpretation of parametric data.

This study will specifically focus on VoIP
services and architectures. These are based on IMS solutions (SIP protocol)
provided by a few technology vendors to France Telecom/Orange. The
extrapolation of the results of this study to general (and even standardisable)
rules of diagnostic (in general highly dependent on specific characteristics of
services and networks) is not easy to foresee, and therefore we won’t try to
work on that direction.

This work will be undertaken in close cooperation
with (in a first time) our searchers specialised in the development of
algorithms and models for voice signal processing (voice quality measurement,
speech coding, voice enhancements) and (afterwards) with operational teams
having the knowledge of network equipments characteristics and able to provide
data on real incidents necessary to set up diagnostic rules.

Methodological approach proposed by the supervisor

We foresee two steps:

-detection in speech signal of perceived and
annoying degradations, classified in general categories:

ocuts in the signal, loss of information

odistortion of the audio signal

odifferent types of noises

osignal level modifications

ovarious impairments linked with interaction
issues (e.g. echo)

-determination of more detailed sub-categories
(e.g. for noise: distinction according to spectral content and level), linked
with known and identified technical causes

The first step is clearly and purely signal
processing oriented. We must mention that recent PhD. studies (e.g. M.
Wältermann at DT, N. Côté and A. Leman at FT) started this work and already
determined degradation categories (for listening-only contexts), as well as
first (still perfectible) detection algorithms.

The second step is more the focus of the current
study. It will combine the existing algorithms (or enhancements of them) with
the analysis of IP parametric information (packet loss ratio and its time
repartition, network equipments counters or trouble tickets, measurements
performed on terminals, etc.)

Global schedule

The time schedule will follow the distinction in
two steps exposed before:

-Enhancement of existing algorithms, to allow
detection of sub-categories as well as “recognition” of the signature of some
signal processing features (noise reduction, coding and transcoding, etc.). This
is the hardest and longest part of the study.

-Setting up of diagnostic rules to link these new
sub-categories to real technical issues, thanks to a combination of
measurements on signal and of parametric data. An expert system based on
neuronal networks is foreseen, but other approaches can be envisaged as well.